This disclosure relates generally to the field of image processing, and more particularly to a system and a method for residual analysis of images.
In many conventional image processing scenarios comprising hyperspectral imaging (HSI) systems, hyperspectral sensors collect data of an image from one spatial line and disperse the spectrum across a perpendicular direction of the focal plane of the optics receiving the image. Thus a focal plane pixel measures the intensity of a given spot on the ground in a specific waveband. A complete HSI cube scene is formed by scanning this spatial line across the scene that is imaged. The complete HSI cube may be analyzed as a measurement of the spectrum, the intensity in many wavebands, for a spatial pixel. This spatial pixel represents a given spot on the ground in a cross-scan direction for one of the lines at a given time in the scan direction. These spectra are analyzed to detect targets or spectral anomalies. Some of the focal plane pixels may change in gain and/or offset since they were last calibrated. The offset and gain errors for such pixels result in measurement biases in the specific waveband and cross-scan location associated with that focal plane pixel. These biases will affect the values of target and anomaly filters and may also result in false alarms for target or spectral anomaly detection. Since every focal plane pixel is scanned across the scene, these poorly calibrated pixels will manifest themselves as stripes in the target and anomaly scores for the scene. These stripes can interfere with target or anomaly detection algorithms as well as data compression algorithms and limit mission performance. Accordingly, there is a need for on-platform scene based non-uniformity correction of pixels in an inexpensive and computationally fast manner.